Many immunization strategies have been proposed to prevent infectious viruses from spreading through a network. In this study, we propose efficient immunization strategies to prevent a default contagion that might occur in a financial network. An essential difference from the previous studies on immunization strategy i…
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48 results for “immunization”
Study on the probability of immunity and its bounds.
problem Estimating the probability of immunity and its bounds.
method Derive necessary and sufficient conditions for non-immunity and ε-bounded immunity; introduce indirect immunity; propose sensitivity analysis.
result Estimate the probability of benefit and produce tighter bounds of the probability of benefit.
AdvImmune improves certifiable robustness of GNNs against adversarial attacks.
problem Vulnerability of graph neural networks to adversarial attacks.
method Proposes AdvImmune, an algorithm that optimizes with meta-gradient to improve certifiable robustness.
result Remarkably improves the ratio of robust nodes by 12%, 42%, 65% with an affordable immune budget of only 5% edges.
New foundation for Shapley value immune to coalitional manipulations.
problem Coalitional manipulations in game theory.
method Revised Shapley value criteria for coalitional games.
result Shapley value is immune to coalitional manipulations under new criteria.
In their seminal work Carr and Lee (2008) show how to robustly price and replicate a variety of claims written on the quadratic variation of a risky asset under the assumption that the asset's volatility process is independent of the Brownian motion that drives the asset's price. Additionally, they propose a correlatio…
Estimates vaccine effectiveness and immune correlates in TND studies with missing data.
problem Confounding and missing data in TND studies of vaccine effectiveness and immune correlates.
method Targeted maximum likelihood estimation using a semiparametric logistic regression model.
result Valid causal inference of vaccine effectiveness and immune correlates in TND studies with missing exposure data.
DeepRC uses Hopfield networks and attention to classify immune repertoires.
problem Classifying the vast number of immunosequences of an individual.
method Integrates transformer-like attention into deep learning architectures.
result DeepRC outperforms other methods in predictive performance.
As adversarial attacks pose a serious threat to the security of AI system in practice, such attacks have been extensively studied in the context of computer vision applications. However, few attentions have been paid to the adversarial research on automatic path finding. In this paper, we show dominant adversarial exam…
Paper uses bond pricing and convexity adjustments to explain herd immunity paradox.
problem Early onset of herd immunity contradicts R value estimates from early stage growth.
method Utilizes Vasicek's bond pricing formula and de Finetti's Theorem approach.
result Reduces modeling discrepancy to simple convexity formulas.
Feature selection predicts immune state changes in RA mouse model.
problem Predicting the immune state change after RA immunotherapy.
method Feature selection algorithms applied to mouse CIA model data.
result Selected features predict both T cell markers and treatment efficacy.
This paper analyses the Chinese Sovereign bond yield to find out the principal factors affecting the term structure of interest rate changes. We apply Principal Component Analysis (PCA) on our data consisting of the Chinese Sovereign bond from January 2002 till May 2018 with the different yield to maturity. Then we wil…
Background. In Italy, in recent years, vaccination coverage for key immunizations as MMR has been declining to worryingly low levels. In 2017, the Italian Gov't expanded the number of mandatory immunizations introducing penalties to unvaccinated children's families. During the 2018 general elections campaign, immunizat…
Bayesian optimization of antibodies learns from immune system evolution.
problem Efficiently optimizing antibody sequences in a large space of possibilities.
method Bayesian optimization guided by a generative model of evolving antibody sequences.
result CloneBO optimizes antibodies more efficiently than previous methods.
In this paper we outline initial concepts for an immune inspired algorithm to evaluate price time series data. The proposed solution evolves a short term pool of trackers dynamically through a process of proliferation and mutation, with each member attempting to map to trends in price movements. Successful trackers fee…
A new PGA algorithm ensures stable, robust, and noise-immune solutions for non-negative inverse problems.
problem Stable convergence and suboptimal solutions in inverse problems due to negative values and high sensitivity to hyperparameters.
method A novel multiplicative update proximal gradient algorithm (SSO-PGA) that enforces non-negativity and boundedness through a learnable sigmoid-based operator.
result Significantly surpasses traditional PGA and other state-of-the-art algorithms in performance and stability.
A time-dependent SIR model predicts COVID-19 spread and herd immunity.
problem Modeling and predicting COVID-19 spread and herd immunity.
method Time-dependent SIR model with 2 types of infected persons (detectable and undetectable).
result The model accurately predicts the turning point and herd immunity threshold.
Major histocompatibility complex class two (MHC-II) molecules are trans-membrane proteins and key components of the cellular immune system. Upon recognition of foreign peptides expressed on the MHC-II binding groove, helper T cells mount an immune response against invading pathogens. Therefore, mechanistic identificati…
Method estimates shared and study-specific factors for multi-study data.
problem Covariance estimation for multi-study data with shared and study-specific components.
method Spectral decomposition for latent factors, surrogate Bayesian regressions for loadings and variances.
result Strong frequentist guarantees and superior performance in simulations and real data.
Machine learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signalling, like in early immune recognition. We draw a formal analogy between neural networks used in machine learning and model…
We develop a novel multi-fidelity framework that goes far beyond the classical AR(1) Co-kriging scheme of Kennedy and O'Hagan (2000). Our method can handle general discontinuous cross-correlations among systems with different levels of fidelity. A combination of multi-fidelity Gaussian Processes (AR(1) Co-kriging) and …
Multinomial logistic model for coinfection diagnosis between arbovirus and malaria in Kedougoustat.AP
In tropical regions, populations continue to suffer morbidity and mortality from malaria and arboviral diseases. In Kedougou (Senegal), these illnesses are all endemic due to the climate and its geographical position. The co-circulation of malaria parasites and arboviruses can explain the observation of coinfected case…
New algorithm improves GAN stability and quality.
problem Training GANs is unstable and computationally expensive.
method Inspired by host-pathogen co-evolution, proposes a new algorithm.
result Increased stability and better image generation with less computing.
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence paradigm focusing on the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. There are several paradigms of CI [like artificial neural networks, evolutionary computations, swarm intelli…
We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most and least impacted units.…
Current Neural Architecture Search techniques can suffer from a few shortcomings, including high computational cost, excessive bias from the search space, conceptual complexity or uncertain empirical benefits over random search. In this paper, we present ImmuNeCS, an attempt at addressing these issues with a method tha…
Topological data analysis aims to extract topological quantities from data, which tend to focus on the broader global structure of the data rather than local information. The Mapper method, specifically, generalizes clustering methods to identify significant global mathematical structures, which are out of reach of man…
New framework boosts neural network performance and resilience.
problem Susceptibility of compact neural network implementations to system disturbances.
method Realistic crossbar simulations and Mosaics framework to re-use synaptic connections.
result Compact neural networks are noise-immune and perform well under disturbances.
Minimax Optimal Rates of Estimation in High Dimensional Additive Models: Universal Phase Transitionmath.ST
We establish minimax optimal rates of convergence for estimation in a high dimensional additive model assuming that it is approximately sparse. Our results reveal an interesting phase transition behavior universal to this class of high dimensional problems. In the {\it sparse regime} when the components are sufficientl…
Phylogenetic tree inference using deep DNA sequencing is reshaping our understanding of rapidly evolving systems, such as the within-host battle between viruses and the immune system. Densely sampled phylogenetic trees can contain special features, including "sampled ancestors" in which we sequence a genotype along wit…
Model predicts credit portfolio losses with contagion effects.
problem Predicting credit portfolio losses with contagion effects.
method Introduced a model with a recursive algorithm and flexible distributions.
result Good fit for synthetic CDO tranches of the iTraxx index.
Defense against ASR attacks using dropout uncertainty.
problem Adversarial attacks on ASR systems.
method Dropout uncertainty in neural networks.
result High detection accuracy across various ASR systems and datasets.
Improved GPLVM model for single-cell RNA-seq data.
problem Lack of effective scalable models for clustering cell types in large-scale single-cell RNA-seq data.
method Introduces amortized stochastic variational Bayesian GPLVM (BGPLVM) tailored for single-cell RNA-seq.
result Matches the performance of scVI on synthetic and real-world datasets and reveals more interpretable latent structures.
Wiener-Granger causality is a widely used framework of causal analysis for temporally resolved events. We introduce a new measure of Wiener-Granger causality based on kernelization of partial canonical correlation analysis with specific advantages in the context of large high-dimensional data. The introduced measure is…
Antibodies are a critical part of the immune system, having the function of directly neutralising or tagging undesirable objects (the antigens) for future destruction. Being able to predict which amino acids belong to the paratope, the region on the antibody which binds to the antigen, can facilitate antibody design an…
Infinite dimensional portfolio representation as applied to model points selection in life insuranceq-fin.PR
We consider the problem of seeking an optimal set of model points associated to a fixed portfolio of life insurance policies. Such an optimal set is characterized by minimizing a certain risk functional, which gauges the average discrepancy with the fixed portfolio in terms of the fluctuation of the interest rate term …
Novel ML approach optimizes large portfolios without covariance matrix issues.
problem Static and dynamic portfolio optimization for many assets.
method Machine learning for constrained optimization, avoiding covariance matrix computation.
result Significant excess returns in U.S. and China equity markets.
The Knowledge Base (KB) used for real-world applications, such as booking a movie or restaurant reservation, keeps changing over time. End-to-end neural networks trained for these task-oriented dialogs are expected to be immune to any changes in the KB. However, existing approaches breakdown when asked to handle such c…
Proposes a bond portfolio solution for managing interest rate risk.
problem Managing long-term assets and liabilities under interest rate risk.
method Proposes a bond portfolio solution based on ambiguity-averse preferences, accommodating various constraints and interest rate perturbations.
result Optimal portfolio can be computed as a simple generalized least squares problem, enhancing out-of-sample performance.
Information Cascades Model captures dynamical properties of user activity in a social network. In this work, we develop a novel framework for activity shaping under the Continuous-Time Information Cascades Model which allows the administrator for local control actions by allocating targeted resources that can alter the…
Topologically protected vortex knots and linkscond-mat.quant-gas
Topologically protected vortex knots and links are proposed and proven.
problem Decaying of tangled vortex structures through local reconnections and strand crossings.
method Proposed and proven topologically protected vortex structures using non-Abelian topological vortices.
result Existence of topologically protected -colored links and classification using the -invariant.
The study examines how model predictions hold up under model extensions.
problem Model predictions may not be robust under model extensions, limiting their applicability.
method The study uses causal ordering to assess robustness of qualitative model predictions and characterizes model extensions that preserve predictions.
result Conditions and techniques are provided to assess robustness of model predictions under model extensions.
We study the performance of stochastically trained deep neural networks (DNNs) whose synaptic weights are implemented using emerging memristive devices that exhibit limited dynamic range, resolution, and variability in their programming characteristics. We show that a key device parameter to optimize the learning effic…
In recent years, neural networks have demonstrated outstanding effectiveness in a large amount of applications.However, recent works have shown that neural networks are susceptible to adversarial examples, indicating possible flaws intrinsic to the network structures. To address this problem and improve the robustness …
We revisit the Blind Deconvolution problem with a focus on understanding its robustness and convergence properties. Provable robustness to noise and other perturbations is receiving recent interest in vision, from obtaining immunity to adversarial attacks to assessing and describing failure modes of algorithms in missi…
We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss correction that are agnostic to both application domain and network architecture. They simply amount to at most a matrix inversion and mult…
New framework tackles deep learning issues like local traps and miscalibration.
problem Local traps and miscalibration in deep neural networks.
method Sparse deep learning framework with prior annealing algorithms.
result Proposed method successfully addresses local traps and miscalibration.
Real world systems typically feature a variety of different dependency types and topologies that complicate model selection for probabilistic graphical models. We introduce the ensemble-of-forests model, a generalization of the ensemble-of-trees model. Our model enables structure learning of Markov random fields (MRF) …
Generalizations of Functionally Generated Portfolios with Applications to Statistical Arbitrageq-fin.PM
The theory of functionally generated portfolios (FGPs) is an aspect of the continuous-time, continuous-path Stochastic Portfolio Theory of Robert Fernholz. FGPs have been formulated to yield a master equation - a description of their return relative to a passive (buy-and-hold) benchmark portfolio serving as the numérai…